Sex-biased gene expression at single-cell resolution: cause and consequence of sexual dimorphism

Gene expression differences between males and females are thought to be key for the evolution of sexual dimorphism, and sex-biased genes are often used to study the molecular footprint of sex-specific selection. However, gene expression is often measured from complex aggregations of diverse cell types, making it difficult to distinguish between sex differences in expression that are due to regulatory rewiring within similar cell types and those that are simply a consequence of developmental differences in cell-type abundance. To determine the role of regulatory versus developmental differences underlying sex-biased gene expression, we use single-cell transcriptomic data from multiple somatic and reproductive tissues of male and female guppies, a species that exhibits extensive phenotypic sexual dimorphism. Our analysis of gene expression at single-cell resolution demonstrates that nonisometric scaling between the cell populations within each tissue and heterogeneity in cell-type abundance between the sexes can influence inferred patterns of sex-biased gene expression by increasing both the false-positive and false-negative rates. Moreover, we show that, at the bulk level, the subset of sex-biased genes that are the product of sex differences in cell-type abundance can significantly confound patterns of coding-sequence evolution. Taken together, our results offer a unique insight into the effects of allometry and cellular heterogeneity on perceived patterns of sex-biased gene expression and highlight the power of single-cell RNA-sequencing in distinguishing between sex-biased genes that are the result of regulatory change and those that stem from sex differences in cell-type abundance, and hence are a consequence rather than a cause of sexual dimorphism.

Single-cell transcriptomic analysis of oral masticatory and lining mucosa-derived mesenchymal stromal cells

AIM

To reveal the heterogeneity of ex vivo-cultured human mesenchymal stromal cells derived from either masticatory or lining oral mucosa.

MATERIALS AND METHODS

Cells were retrieved from the lamina propria of the hard palate and alveolar mucosa of three individuals. The analysis of transcriptomic-level differences was accomplished using single-cell RNA sequencing.

RESULTS

Cluster analysis clearly distinguished between cells from the masticatory and lining oral mucosa, and revealed 11 distinct cell sub-populations, annotated as fibroblasts, smooth muscle cells or mesenchymal stem cells. Interestingly, cells presenting a mesenchymal stem cell-like gene expression pattern were predominantly found in masticatory mucosa. Although cells of masticatory mucosa origin were highly enriched for biological processes associated with wound healing, those from the lining oral mucosa were highly enriched for biological processes associated with the regulation of epithelial cells.

CONCLUSIONS

Our previous work had shown that cells from the lining and masticatory oral mucosae are phenotypically heterogeneous. Here, we extend these findings to show that these changes are not the result of differences in averages but rather represent two distinct cell populations, with mesenchymal stem cells more common in masticatory mucosa. These features may contribute to specific physiological functions and have relevance for potential therapeutic interventions.

Single-cell RNA sequencing reveals the immune microenvironment and signaling networks in cystitis glandularis

Introduction

Cystitis glandularis (CG) is a rare chronic bladder hyperplastic disease that mainly manifests by recurrent frequent urination, dysuria and gross hematuria. The current lack of unified diagnosis and treatment criteria makes it essential to comprehensively describe the inflammatory immune environment in CG research.

Methods

Here, we performed scRNA-sequencing in CG patients for the first time, in which four inflamed tissues as well as three surrounding normal bladder mucosa tissues were included. Specifically, we isolated 18,869 cells to conduct bioinformatic analysis and performed immunofluorescence experiments.

Results

Our genetic results demonstrate that CG does not have the classic chromosomal variation observed in bladder tumors, reveal the specific effects of TNF in KRT15 epithelial cells, and identify a new population of PIGR epithelial cells with high immunogenicity. In addition, we confirmed the activation difference of various kinds of T cells during chronic bladder inflammation and discovered a new group of CD27-Switch memory B cells expressing a variety of immunoglobulins.

Discussion

CG was regarded as a rare disease and its basic study is still weak.Our study reveals, for the first time, the different kinds of cell subgroups in CG and provides the necessary basis for the clinical treatment of cystitis glandularis. Besides, our study significantly advances the research on cystitis glandularis at the cellular level and provides a theoretical basis for the future treatment of cystitis glandularis.

The Applications of Single-Cell RNA Sequencing in Atherosclerotic Disease

Atherosclerosis still is the primary cause of death worldwide. Our characterization of the atherosclerotic lesion is mainly rooted in definitions based on pathological descriptions. We often speak in absolutes regarding plaque phenotypes: vulnerable vs. stable plaques or plaque rupture vs. plaque erosion. By focusing on these concepts, we may have oversimplified the atherosclerotic disease and its mechanisms. The widely used definitions of pathology-based plaque phenotypes can be fine-tuned with observations made with various -omics techniques. Recent advancements in single-cell transcriptomics provide the opportunity to characterize the cellular composition of the atherosclerotic plaque. This additional layer of information facilitates the in-depth characterization of the atherosclerotic plaque. In this review, we discuss the impact that single-cell transcriptomics may exert on our current understanding of atherosclerosis.

Vertical sleeve gastrectomy triggers fast β-cell recovery upon overt diabetes

OBJECTIVE

The effectiveness of bariatric surgery in restoring β-cell function has been described in type-2 diabetes (T2D) patients and animal models for years, whereas the mechanistic underpinnings are largely unknown. The possibility of vertical sleeve gastrectomy (VSG) to rescue far-progressed, clinically-relevant T2D and to promote β-cell recovery has not been investigated on a single-cell level. Nevertheless, characterization of the heterogeneity and functional states of β-cells after VSG is a fundamental step to understand mechanisms of glycaemic recovery and to ultimately develop alternative, less-invasive therapies.

METHODS

We performed VSG in late-stage diabetic db/db mice and analyzed the islet transcriptome using single-cell RNA sequencing (scRNA-seq). Immunohistochemical analyses and quantification of β-cell area and proliferation complement our findings from scRNA-seq.

RESULTS

We report that VSG was superior to calorie restriction in late-stage T2D and rapidly restored normoglycaemia in morbidly obese and overt diabetic db/db mice. Single-cell profiling of islets of Langerhans showed that VSG induced distinct, intrinsic changes in the β-cell transcriptome, but not in that of α-, δ-, and PP-cells. VSG triggered fast β-cell redifferentiation and functional improvement within only two weeks of intervention, which is not seen upon calorie restriction. Furthermore, VSG expanded β-cell area by means of redifferentiation and by creating a proliferation competent β-cell state.

CONCLUSION

Collectively, our study reveals the superiority of VSG in the remission of far-progressed T2D and presents paths of β-cell regeneration and molecular pathways underlying the glycaemic benefits of VSG.

Transcriptional Differences in Lipid-Metabolizing Enzymes in Murine Sebocytes Derived from Sebaceous Glands of the Skin and Preputial Glands

Sebaceous glands are adnexal structures, which critically contribute to skin homeostasis and the establishment of a functional epidermal barrier. Sebocytes, the main cell population found within the sebaceous glands, are highly specialized lipid-producing cells. Sebaceous gland-resembling tissue structures are also found in male rodents in the form of preputial glands. Similar to sebaceous glands, they are composed of lipid-specialized sebocytes. Due to a lack of adequate organ culture models for skin sebaceous glands and the fact that preputial glands are much larger and easier to handle, previous studies used preputial glands as a model for skin sebaceous glands. Here, we compared both types of sebocytes, using a single-cell RNA sequencing approach, to unravel potential similarities and differences between the two sebocyte populations. In spite of common gene expression patterns due to general lipid-producing properties, we found significant differences in the expression levels of genes encoding enzymes involved in the biogenesis of specialized lipid classes. Specifically, genes critically involved in the mevalonate pathway, including squalene synthase, as well as the sphingolipid salvage pathway, such as ceramide synthase, (acid) sphingomyelinase or acid and alkaline ceramidases, were significantly less expressed by preputial gland sebocytes. Together, our data revealed tissue-specific sebocyte populations, indicating major developmental, functional as well as biosynthetic differences between both glands. The use of preputial glands as a surrogate model to study skin sebaceous glands is therefore limited, and major differences between both glands need to be carefully considered before planning an experiment.

Myeloid Heterogeneity in Kidney Disease as Revealed through Single-Cell RNA Sequencing

Kidney disease represents a global health burden of increasing prevalence and is an independent risk factor for cardiovascular disease. Myeloid cells are a major cellular compartment of the immune system; they are found in the healthy kidney and in increased numbers in the damaged and/or diseased kidney, where they act as key players in the progression of injury, inflammation, and fibrosis. They possess enormous plasticity and heterogeneity, adopting different phenotypic and functional characteristics in response to stimuli in the local milieu. Although this inherent complexity remains to be fully understood in the kidney, advances in single-cell genomics promise to change this. Specifically, single-cell RNA sequencing (scRNA-seq) has had a transformative effect on kidney research, enabling the profiling and analysis of the transcriptomes of single cells at unprecedented resolution and throughput, and subsequent generation of cell atlases. Moving forward, combining scRNA- and single-nuclear RNA-seq with greater-resolution spatial transcriptomics will allow spatial mapping of kidney disease of varying etiology to further reveal the patterning of immune cells and nonimmune renal cells. This review summarizes the roles of myeloid cells in kidney health and disease, the experimental workflow in currently available scRNA-seq technologies, and published findings using scRNA-seq in the context of myeloid cells and the kidney.

Single-Cell RNA Sequencing Reveals Renal Endothelium Heterogeneity and Metabolic Adaptation to Water Deprivation

BACKGROUND

Renal endothelial cells from glomerular, cortical, and medullary kidney compartments are exposed to different microenvironmental conditions and support specific kidney processes. However, the heterogeneous phenotypes of these cells remain incompletely inventoried. Osmotic homeostasis is vitally important for regulating cell volume and function, and in mammals, osmotic equilibrium is regulated through the countercurrent system in the renal medulla, where water exchange through endothelium occurs against an osmotic pressure gradient. Dehydration exposes medullary renal endothelial cells to extreme hyperosmolarity, and how these cells adapt to and survive in this hypertonic milieu is unknown.

METHODS

We inventoried renal endothelial cell heterogeneity by single-cell RNA sequencing >40,000 mouse renal endothelial cells, and studied transcriptome changes during osmotic adaptation upon water deprivation. We validated our findings by immunostaining and functionally by targeting oxidative phosphorylation in a hyperosmolarity model in vitro and in dehydrated mice in vivo.

RESULTS

We identified 24 renal endothelial cell phenotypes (of which eight were novel), highlighting extensive heterogeneity of these cells between and within the cortex, glomeruli, and medulla. In response to dehydration and hypertonicity, medullary renal endothelial cells upregulated the expression of genes involved in the hypoxia response, glycolysis, and-surprisingly-oxidative phosphorylation. Endothelial cells increased oxygen consumption when exposed to hyperosmolarity, whereas blocking oxidative phosphorylation compromised endothelial cell viability during hyperosmotic stress and impaired urine concentration during dehydration.

CONCLUSIONS

This study provides a high-resolution atlas of the renal endothelium and highlights extensive renal endothelial cell phenotypic heterogeneity, as well as a previously unrecognized role of oxidative phosphorylation in the metabolic adaptation of medullary renal endothelial cells to water deprivation.

Lineage Tracing: Computational Reconstruction Goes Beyond the Limit of Imaging

Tracking the fate of individual cells and their progeny through lineage tracing has been widely used to investigate various biological processes including embryonic development, homeostatic tissue turnover, and stem cell function in regeneration and disease. Conventional lineage tracing involves the marking of cells either with dyes or nucleoside analogues or genetic marking with fluorescent and/or colorimetric protein reporters. Both are imaging-based approaches that have played a crucial role in the field of developmental biology as well as adult stem cell biology. However, imaging-based lineage tracing approaches are limited by their scalability and the lack of molecular information underlying fate transitions. Recently, computational biology approaches have been combined with diverse tracing methods to overcome these limitations and so provide high-order scalability and a wealth of molecular information. In this review, we will introduce such novel computational methods, starting from single-cell RNA sequencing-based lineage analysis to DNA barcoding or genetic scar analysis. These novel approaches are complementary to conventional imaging-based approaches and enable us to study the lineage relationships of numerous cell types during vertebrate, and in particular human, development and disease.